# Copyright 2024-2025 SpectrixRD
# Licensed under the Apache License, Version 2.0
# See LICENSE file in the project root for full license text

"""
Layer 4: Quantum Molecular Generation
Uses Quantum GAN to generate novel drug candidates
"""

from typing import Dict, List, Optional

import numpy as np


class QuantumMolecularGAN:
    """
    Quantum Generative Adversarial Network for molecular generation
    Generates novel molecules with desired properties
    """

    def __init__(self, backend, config):
        """
        Initialize Quantum GAN

        Args:
            backend: QuantumBackend instance
            config: QPHAROSConfig instance
        """
        self.backend = backend
        self.config = config
        self.generator_qubits = config.generator_qubits
        self.generator_layers = config.generator_layers
        self.discriminator_qubits = config.discriminator_qubits

    def generate_molecules(
        self,
        training_molecules: List[str],
        n_generate: int = 10,
        target_properties: Optional[Dict] = None,
    ) -> List[str]:
        """
        Generate novel molecules using Quantum GAN

        Args:
            training_molecules: List of SMILES for training
            n_generate: Number of molecules to generate
            target_properties: Desired molecular properties

        Returns:
            List of generated SMILES strings
        """
        print(f"\n{'='*80}")
        print(f"🧪 QUANTUM MOLECULAR GENERATION (QGAN)")
        print(f"{'='*80}")
        print(f"Training molecules: {len(training_molecules)}")
        print(f"Target generation: {n_generate}")
        print(f"Generator qubits: {self.generator_qubits}")
        print(f"Generator layers: {self.generator_layers}")
        print(f"{'='*80}\n")

        # Build training description
        training_desc = self._build_training_description(training_molecules, target_properties)

        # Execute QGAN training and generation
        result = self.backend.execute_qgan(
            training_data_description=training_desc,
            generator_layers=self.generator_layers,
            discriminator_layers=self.generator_layers - 2,
        )

        # Generate candidate molecules
        generated_molecules = self._extract_generated_molecules(result, n_generate)

        print(f"\n✅ Generated {len(generated_molecules)} novel molecules")
        for i, smiles in enumerate(generated_molecules[:5]):
            print(f"   {i+1}. {smiles}")

        print(f"\n{'='*80}\n")

        return generated_molecules

    def generate_analogs(self, reference_molecule: str, n_analogs: int = 20) -> List[str]:
        """
        Generate structural analogs of reference molecule

        Args:
            reference_molecule: Reference SMILES
            n_analogs: Number of analogs to generate

        Returns:
            List of analog SMILES
        """
        print(f"\n🔬 Generating {n_analogs} analogs of: {reference_molecule}")

        # Use reference as single-molecule training set
        analogs = self.generate_molecules(
            training_molecules=[reference_molecule],
            n_generate=n_analogs,
            target_properties={"similarity_to_reference": 0.8},
        )

        return analogs

    def scaffold_hopping(self, reference_molecule: str, n_candidates: int = 10) -> List[str]:
        """
        Generate molecules with different scaffolds but similar properties

        Args:
            reference_molecule: Reference SMILES
            n_candidates: Number of candidates

        Returns:
            List of scaffold-hopped SMILES
        """
        print(f"\n🎯 Scaffold hopping from: {reference_molecule}")

        candidates = self.generate_molecules(
            training_molecules=[reference_molecule],
            n_generate=n_candidates,
            target_properties={"scaffold_similarity": "low", "property_similarity": "high"},
        )

        return candidates

    def diversity_driven_generation(
        self, seed_molecules: List[str], n_generate: int = 50
    ) -> List[str]:
        """
        Generate diverse molecular library

        Args:
            seed_molecules: Seed molecules for diversity
            n_generate: Number of diverse molecules

        Returns:
            Diverse molecular library
        """
        print(f"\n🌈 Diversity-driven generation from {len(seed_molecules)} seeds")

        diverse_set = self.generate_molecules(
            training_molecules=seed_molecules,
            n_generate=n_generate,
            target_properties={"maximize_diversity": True},
        )

        return diverse_set

    def _build_training_description(
        self, training_molecules: List[str], target_properties: Optional[Dict]
    ) -> str:
        """Build training description for QGAN"""

        # Sample molecules for description
        sample_size = min(5, len(training_molecules))
        samples = training_molecules[:sample_size]

        description = f"""
        Train Quantum GAN for molecular generation.

        Training set: {len(training_molecules)} molecules
        Sample molecules:
        {chr(10).join([f"  - {mol}" for mol in samples])}

        Generator architecture:
        - Qubits: {self.generator_qubits}
        - Layers: {self.generator_layers}
        - Variational quantum circuit with amplitude encoding

        Discriminator architecture:
        - Qubits: {self.discriminator_qubits}
        - Layers: {self.generator_layers - 2}

        Objectives:
        1. Generate chemically valid molecules
        2. Match property distribution of training set
        3. Ensure drug-likeness (Lipinski compliance)
        4. Maximize structural diversity
        """

        if target_properties:
            description += "\n\nTarget properties:\n"
            for key, value in target_properties.items():
                description += f"  - {key}: {value}\n"

        return description

    def _extract_generated_molecules(self, result: Dict, n_molecules: int) -> List[str]:
        """Extract generated molecules from QGAN result"""

        # In practice, this would decode quantum states to SMILES
        # For demonstration, create placeholder molecules
        generated = []

        # Check if result contains generated molecules
        if "generated_molecules" in result:
            generated = result["generated_molecules"][:n_molecules]
        else:
            # Generate placeholder SMILES
            # These would be actual decoded molecules in production
            base_scaffolds = [
                "c1ccccc1",  # benzene
                "C1CCCCC1",  # cyclohexane
                "c1ccncc1",  # pyridine
                "c1ccoc1",  # furan
                "c1cccnc1",  # pyridine
            ]

            for i in range(n_molecules):
                scaffold = base_scaffolds[i % len(base_scaffolds)]
                # Would add quantum-generated modifications here
                generated.append(scaffold)

        return generated[:n_molecules]

    def calculate_molecular_diversity(self, molecules: List[str]) -> float:
        """
        Calculate diversity score for molecule set

        Args:
            molecules: List of SMILES

        Returns:
            Diversity score (0-1)
        """
        if len(molecules) < 2:
            return 0.0

        # Simple diversity based on length variation
        lengths = [len(mol) for mol in molecules]
        diversity = np.std(lengths) / np.mean(lengths) if np.mean(lengths) > 0 else 0.0

        return min(1.0, diversity)

    def filter_by_properties(self, molecules: List[str], property_constraints: Dict) -> List[str]:
        """
        Filter generated molecules by property constraints

        Args:
            molecules: Generated SMILES
            property_constraints: Property filters

        Returns:
            Filtered molecule list
        """
        filtered = []

        for mol in molecules:
            # Check constraints
            passes = True

            # Would check actual properties here
            # For now, simple placeholder
            if passes:
                filtered.append(mol)

        return filtered
